Introduction to neural and cognitive modeling / Daniel S. Levine.

Includes bibliographical references and index.

Part I: Foundations of Neural Network Theory

Chapter 1: Neural Networks for Modeling Behavior

Chapter 2: Historical Outline

Chapter 3: Associative Learning and Synaptic Plasticity

Chapter 4: Competition, Lateral Inhibition, and Short-Term Memory

Part II: Computational Cognitive Neuroscience

Chapter 5: Progress in Cognitive Neuroscience

Chapter 6: Models of Conditioning and Reinforcement Learning

Chapter 7: Models of Coding, Categorization, and Unsupervised Learning

Chapter 8: Models of Supervised Pattern and Category Learning

Chapter 9: Models of Complex Mental Functions

Appendices

Appendix 1: Mathematical Techniques for Neural Networks

Appendix 2: Basic Facts of Neurobiology

References

This textbook provides a general introduction to the field of neural networks. Thoroughly revised and updated from the previous editions of 1991 and 2000, the current edition concentrates on networks for modeling brain processes involved in cognitive and behavioral functions. Part one explores the philosophy of modeling and the field’s history starting from the mid-1940s, and then discusses past models of associative learning and of short-term memory that provide building blocks for more complex recent models. Part two of the book reviews recent experimental findings in cognitive neuroscience and discusses models of conditioning, categorization, category learning, vision, visual attention, sequence learning, behavioral control, decision making, reasoning, and creativity. The book presents these models both as abstract ideas and through examples and concrete data for specific brain regions.

The book includes two appendices to help ground the reader: one reviewing the mathematics used in network modeling, and a second reviewing basic neuroscience at both the neuron and brain region level. The book also includes equations, practice exercises, and thought experiments.